This paper studies marijuana-related tweets in social network Twitter. We collected more than 300,000 marijuana related tweets during November 2016 in our study. Our text-mining based algorithms and data analysis unveil some interesting patterns including: (i) users' attitudes (e.g., positive or negative) can be characterized by the existence of outer links in a tweet; (ii) 67% users use their mobile phones to post their messages while many users publish their messages using third-party automatic posting services; and (3) the number of tweets during weekends is much higher than during weekdays. Our data also showed the impact of the political events such as the U.S. presidential election or state marijuana legalization votes on the marijuana-related tweeting frequencies.